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Record W4401792744 · doi:10.15402/esj.v10i2.70858

Intersectionality in Housing Research: Early Reflections from a Community-based Participatory Research Partnership

2024· article· en· W4401792744 on OpenAlexaffvenue
Katie MacDonald, Sara Dorow, Reisa Klein, Olesya Kochkina

Bibliographic record

VenueEngaged Scholar Journal Community-Engaged Research Teaching and Learning · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsUniversity of AlbertaAthabasca University
Fundersnot available
KeywordsIntersectionalityGeneral partnershipParticipatory action researchCommunity-based participatory researchSociologyCitizen journalismGender studiesPolitical sciencePublic relationsAnthropology

Abstract

fetched live from OpenAlex

We discuss early reflections of a housing security research project focused on implementing intersectional praxis across the life cycle of community-based participatory research. Drawing on our team’s initial Co-learning Workshop, including community and academic partners, we share initial learnings of our collective engagement with the connections between intersectionality and housing security. Specifically, we reflect on three key challenges, or “hopeful hesitations,” that have emerged as we begin our collaboration: co-defining intersectionality (including both theory and implementation); integrating intersectionality into the multi-scaled complexities of housing security; and communicating the relevance of intersectionality to a wider network of actors in housing security policy and programming. We suggest that an intersectional lens is crucial to housing security work because of the ways it attends to the everyday lived experiences of housing insecurity, the interlocking forms of oppression that create differentiated experiences, and the specific contexts in which structural housing inequities take root.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.979
metaresearch head score (Gemma)0.863
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity
Consensus categoriesMetaresearch, Science and technology studies
DomainCandidate signal: Methods · Consensus signal: Methods
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.853
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.9790.863
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0050.005
Science and technology studies0.6160.005
Scholarly communication0.0070.002
Open science0.0020.001
Research integrity0.0010.853
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.932
GPT teacher head0.702
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designQualitative
DomainMethods
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2024
Admission routes2
Has abstractyes

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